Low power device with variable scheduling

ABSTRACT

Medical device systems and methods for operating medical device systems conserve energy by efficiently managing computational demands of the systems. Signals from a subject are processed and analyzed and an estimate of a propensity for a subject to have a neurological event is determined. Based on the results of the analysis and the estimate, further analysis may be performed and the estimate may be refined. Succeeding cycles of signal measurement and analysis are scheduled depending on the results of the analysis and the estimate. The schedule may be varied temporally or with regard to the types and intensities of analyzes performed.

FIELD

The present invention relates generally to medical device systems.

BACKGROUND

A variety of medical device systems are used to measure physiological signals from a subject and to process the signals and provide indications of potential or actual problem conditions. Computational demands of processing systems associated with the medical devices systems produce drains on the power sources of these device systems and can have a major impact on overall battery life. Moreover, in many medical device systems, it is desirable to keep the system as small and unobtrusive as possible so that the patient can have it available at all times.

In the case of implantable systems, power source replacement may involve surgery with its attendant costs and risks to the subject. Moreover, power source replacement may involve replacement of the implantable system itself because such units are typically hermetically sealed to reduce the likelihood of infection.

SUMMARY

The invention provides medical device systems and methods for operating medical device systems that provide energy savings by efficiently managing computational demands of the systems. Some such systems comprise detectors to receive input from a subject, communications systems to deliver signals indicative of the input to a processor, processing systems to analyze the signals and determine one or more conditions of the subject, and a communication system to provide indications of the one or more conditions to the subject.

The various embodiments describe ways for a medical device to control the scheduling of signal acquisition from a subject and/or data analysis so as to efficiently manage the use of electrical energy. A set of computer instructions, programmable logic, or circuitry, for example, one or more feature extractors and one or more classifiers process the signals to determine a first estimate of a susceptibility or propensity of the subject to have a neurological event, as in, for example, event prediction or the detection of pre-onset characteristics. If desired, information related to the estimate may be output to the subject. In some embodiments, the first estimate may be used to determine, at least in part, a time schedule for subsequent signal acquisition and/or data analysis. In some embodiments, the first estimate may be used to determine, at least in part, which data analysis methods and systems are to be used for subsequent data analyses. In general, if the first estimate indicates a relatively higher susceptibility of a neurological event within some prediction horizon, signal measurement and/or analysis may be scheduled to occur more frequently and/or more computationally intensive analysis systems may be employed for subsequent data analysis. The computer instructions, programmable logic or circuitry may be enabled on a processing system that is external to the subject, on a processing system that is implanted in the subject, or on combinations of external and implanted processing systems.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating aspects of contingent use of feature extractors and classifiers;

FIG. 2 is an example of a classifier output;

FIG. 3 is a flow chart of an embodiment of variable scheduling;

FIG. 4 is a flow chart of an embodiment combining variable scheduling and contingent analysis;

FIG. 5 is an example timeline for an embodiment of analysis scheduling that varies temporally;

FIG. 6 is a simplified diagram of a system that embodies a contingent scheduling system;

FIG. 7 is a block diagram of an implanted communication unit that may be used in accordance with the systems and methods described herein;

FIG. 8 is a block diagram of an external data device that may be used in accordance with the systems and methods described herein;

FIG. 9 is an example timeline for a typical therapeutic regimen for the treatment of epilepsy; and

FIG. 10 is an example timeline for a therapeutic regimen for the treatment of epilepsy that may be enabled by the system and methods described herein.

DETAILED DESCRIPTION

Certain specific details are set forth in the following description and figures to provide an understanding of various embodiments of the invention. Certain well-known details, associated electronics and medical devices are not set forth in the following disclosure to avoid unnecessarily obscuring the various embodiments of the invention. Further, those of ordinary skill in the relevant art will understand that they can practice other embodiments of the invention without one or more of the details described below. Finally, while various processes are described with reference to steps and sequences in the following disclosure, the description is for providing a clear implementation of particular embodiments of the invention, and the steps and sequences of steps should not be taken as required to practice this invention.

As described in the background, a variety of medical device systems are used to measure physiological signals from a subject and to process those signals. Some such medical device systems, especially those comprising ambulatory and implantable devices, operate on portable power sources such as batteries. Computational processing demands, especially in the case of substantially continuous or repeated monitoring of the subject, can cause energy drains and may have a negative impact on battery life. The various embodiments of the systems and methods provided herein reduce the computational burden on such systems to extend the battery life.

Although some of the discussion below focuses on measuring EEG signals of subjects and subject populations for the detection and prediction of epileptic seizures, it should be appreciated that the invention is not limited to measuring EEG signals or to predicting epileptic seizures. For example, the invention could also be used in systems that measure one or more of a blood pressure, pulse oximetry, temperature of the brain or of portions of the subject, blood flow measurements, ECG/EKG, heart rate signals, respiratory signals, chemical concentrations of neurotransmitters, chemical concentrations of medications, pH in the blood, or other physiological or biochemical parameters of a subject.

Furthermore, aspects of the invention may be useful for monitoring and assisting in the treatments for a variety of conditions such as sleep apnea and other sleep disorders, migraine headaches, depression, Alzheimer's, Parkinson's Disease, dementia, attention deficit disorder, stroke, eating disorders, other neurological or psychiatric disorders, cardiac disease, diabetes, cancer, or the like.

Using epilepsy as an illustrative example, epilepsy is a disorder of the brain characterized by neurological events in the form of chronic, recurring seizures and affects an estimated 50 million people worldwide. Seizures are a result of uncontrolled discharges of electrical activity in the brain. A seizure typically manifests itself as sudden involuntary, disruptive, and often destructive sensory, motor, and cognitive phenomena. Epilepsy is usually treated, though not cured, with medication. Surgery may be indicated in cases in which seizure focus is identifiable, and the seizure focus is not located in the eloquent cortex.

A single neurological event most often does not cause significant morbidity or mortality, but severe or recurring neurological events can result in major medical, social, and economic consequences. Epilepsy is more often diagnosed in children and young adults. People with uncontrolled epilepsy are often significantly limited in their ability to work in many industries and may not be able to legally drive an automobile.

The cause of epilepsy is often uncertain. Symptomatic epilepsies arise due to some structural or metabolic abnormality of the brain and may result from a wide variety of causes including genetic conditions, stroke, head trauma, complications during pregnancy or birth, infections such as bacterial or viral encephalitis, or parasites. Idiopathic epilepsies are those for which no other condition has been implicated as a cause and are often genetic and generalized. In the majority of cases, the cause of a subject's epilepsy is unknown.

One of the most disabling aspects of neurological disorders such as epilepsy is the seeming unpredictability of neurological events such as seizures. Mechanisms underlying the generation of seizures are thought to operate over a period of seconds to minutes before the clinical onset of a seizure. Typically, electrographic manifestations of a neurological event are detectible some time before clinical manifestations occur. Most work in the quantitative analysis of neurological events has been aimed at detecting these electrographic manifestations. NeuroPace, Inc. has been developing systems to detect the electrographic onset of a neurological event so that some action, such as direct electrical stimulation of certain brain structures, may be taken in an attempt to preempt the clinical onset of a neurological event. However, the detection of the electrographic onset of a neurological event may not come far enough in advance of the clinical onset for electrical stimulation or other therapies, such as the administration of anticonvulsant drugs, to be effective in preventing the clinical onset. Additionally, seizure activity may already be causing harm to the brain before the clinical onset of the seizure.

It is desirable to be able to predict neurological events well before their electrographic onset. Embodiments of predictive systems generally comprise a collection of detectors for acquiring data from a subject and an analysis system for processing the data. Predictive analysis systems are routinely considered to be comprised of arrangements of feature extractors and classifiers. Feature extractors are used to quantify or characterize certain aspects of the measured input signals. Classifiers are then used to combine the results obtained from the feature extractors into an overall answer or result. Systems may be designed to detect different types of conditions that may be reflective of neural condition. These could include, but are not limited, to systems designed to detect if the subject's neural condition is indicative of an increased susceptibility or propensity for a neurological event or systems designed to detect deviation from a normal condition. As can be appreciated, for other neurological or non-neurological disorders, the classification of the subject's neural condition will be based on systems, feature extractors and classifiers that are deemed to be relevant to the particular disorder.

FIG. 1 depicts an example of the overall structure of a system for estimating a propensity for the onset of a neurological event such as, for example, an epileptic seizure. The input data 102 may comprise representations of physiological signals obtained from monitoring a subject. Any number of signal channels may be used. Examples of physiological signals that may be used as input data 102 include, but are not limited to, electrical signals generated by electrodes placed on or within the brain or nervous system (EEG signals), temperature of the brain or of portions of the brain, blood pressure or blood flow measurements, pulse oximetry, ECG/EKG, blood pH, chemical concentrations of neurotransmitters, chemical concentrations of medications, combinations of the preceding, and the like.

The input data may be in the form of analog signal data or digital signal data that has been converted by way of an analog to digital converter (not shown). The signals may also be amplified, preprocessed, and/or conditioned to filter out spurious signals or noise. For purposes of simplicity the input data of all of the preceding forms is referred to herein as input data 102.

The input data 102 from the selected physiological signals is supplied to one or more feature extractors 104 a, 104 b, 105. A feature extractor 104 a, 104 b, 105 may be, for example, a set of computer executable instructions stored on a computer readable medium, or a corresponding instantiated object or process that executes on a computing device. Certain feature extractors may also be implemented as programmable logic or as circuitry. In general, a feature extractor 104 a, 104 b, 105 can process data 102 and identify some characteristic of the data 102. Such a characteristic of the data is referred to herein as an extracted feature.

Operation of a feature extractor 104 a, 104 b, 105 requires expenditure of electrical energy to process data and identify characteristics. The amount of electrical energy required may depend on the complexity, quantity, and quality of the input data and on the complexity of the processing system applied to the input data. Feature extractors 104 a, 104 b, 105 that are more complex generally require correspondingly larger amounts of electrical energy. As described more fully below, in some embodiments, some feature extractors 105 may be optionally applied or omitted in various circumstances. For example, when the application of one set of feature extractors 104 a, 104 b is sufficient to estimate that a propensity for a neurological event is sufficiently low, then other feature extractors 105 may not be applied to the input data 102. If the set of feature extractors 104 a, 104 b indicates a higher propensity for a neurological event, then additional feature extractors 105 may be applied to the input data 102.

Each feature extractor 104 a, 104 b, 105 may be univariate (operating on a single input data channel), bivariate (operating on two data channels), or multivariate (operating on multiple data channels). Some examples of potentially useful characteristics to extract from signals for use in determining the subject's propensity for a neurological event, include but are not limited to, alpha band power (8-13 Hz), beta band power (13-18 Hz), delta band power (0.1-4 Hz), theta band power (4-8 Hz), low beta band power (12-15 Hz), mid-beta band power (15-18 Hz), high beta band power (18-30 Hz), gamma band power (30-48 Hz), second, third and fourth (and higher) statistical moments of the EEG amplitudes, spectral edge frequency, decorrelation time, Hjorth mobility (HM), Hjorth complexity (HC), the largest Lyapunov exponent L(max), effective correlation dimension, local flow, entropy, loss of recurrence LR as a measure of non-stationarity, mean phase coherence, conditional probability, brain dynamics (synchronization or desynchronization of neural activity, STLmax, T-index, angular frequency, and entropy), line length calculations, area under the curve, first, second and higher derivatives, integrals, or a combination thereof. Of course, for other neurological conditions, additional or alternative characteristic extractors may be used with the systems described herein.

The extracted characteristics can be supplied to one or more classifiers 106, 107. Like the feature extractors 104 a, 104 b, 105, each classifier 106, 107 may be, for example, a set of computer executable instructions stored on a computer readable medium or a corresponding instantiated object or process that executes on a computing device. Certain classifiers may also be implemented as programmable logic or as circuitry. Operation of a classifier 106, 107 requires electrical energy. The classifiers can vary in complexity. Classifiers 106, 107 that are more complex may require correspondingly larger amounts of electrical energy. In some embodiments, some classifiers may be optionally applied or omitted in various circumstances. For example, when the application of one or more classifiers 106 is sufficient to estimate that a propensity for a neurological event is sufficiently low, then other classifiers 107 may not be applied to the extracted characteristics. If the classifiers 106 indicate a higher propensity for a neurological event, then additional classifiers 107 may be applied to the extracted characteristics.

The classifiers 106, 107 analyze one or more of the extracted characteristics and possibly other subject dependent parameters to provide a result 108 that may characterize, for example, a subject's neural condition. Some examples of classifiers include k-nearest neighbor (“KNN”), neural networks, and support vector machines (“SVM”). Each classifier 106, 107 may provide a variety of output results, such as a logical result or a weighted result. The classifiers 106, 107 may be customized for the individual subject and may be adapted to use only a subset of the characteristics that are most useful for the specific subject. For example, the classifier may detect pre-onset characteristics of a neurological event. Additionally, over time, the classifiers 106, 107 may be further adapted to the subject, based, for example, in part on the result of previous analyses and may reselect extracted characteristics that are used for the specific subject.

As it relates to epilepsy, for example, one implementation of a classification of neural conditions defined by the classifiers 106, 107 may include (1) an inter-ictal condition (sometimes referred to as a “normal” condition), (2) a pre-ictal condition (sometimes referred to as an “abnormal” or “pre-seizure” condition), (3) an ictal condition (sometimes referred to as a “seizure” condition), and (4) a post-ictal condition (sometimes referred to as a “post-seizure” condition). In another embodiment, it may be desirable to have the classifier classify the subject as being in one of two conditions—a pre-ictal condition or inter-ictal condition—which could correspond, respectively, to either an elevated or high propensity for a future seizure or a low propensity for a future seizure.

As noted above, instead of providing a logical answer, it may be desirable for a classifier 106, 107 to provide a weighted answer so as to further delineate within the pre-ictal condition to further allow the system to provide a more specific output communication for the subject. For example, instead of a simple logical answer (e.g., pre-ictal or inter-ictal) it may be desirable to provide a weighted output or other output that quantifies the subject's propensity, probability, likelihood and/or risk of a future neurological event using some predetermined scale (e.g., scale of 1-10, with a “1” meaning “normal” and a “10” meaning a neurological event is imminent). For example, if it is determined that the subject has an increased propensity for a neurological event (e.g., subject has entered the pre-ictal condition), but the neurological event is likely to occur on a long time horizon, the output signal could be weighted to be reflective of the long time horizon, e.g., an output of “5”. However, if the output indicates that the subject is pre-ictal and it is predicted that the neurological event is imminent within the next 10 minutes, the output could be weighted to be reflective of the shorter time horizon to the neurological event, e.g., an output of “9.” On the other hand, if the subject is normal, the system may provide an output of “1”.

Other implementations involve classifier 106 outputs expressing the inter-ictal and pre-ictal conditions as a continuum, with a scalar or vector of parameters describing the actual condition and its variations. FIG. 2 depicts an example of a graphical display of the output of one embodiment of a classifier over a period of time. The output of the classifier at any point in time is a vector of two estimated probabilities: an estimated probability 152 that the input data is indicative of an inter-ictal condition and an estimated probability 154 that the input data is indicative of a pre-ictal condition. The sum of the two probabilities 152, 154, at any given time is one. The estimated probabilities 152, 154 are plotted over a period of time beginning approximately 360 minutes before the onset of a neurological event at time zero, indicated by the vertical axis 150. In the example graph, the estimated probability 152 that the data is indicative of an inter-ictal state was larger than the estimated probability 154 that the data was indicative of a pre-ictal state, until approximately 80 minutes before the onset 150 of the neurological event. The estimated probability 152 then dropped to very small values beginning approximately 50 minutes before the onset 150. The estimated probability 154 that the data is indicative of a pre-ictal state remained low until approximately 80 minutes before the onset 150 of the neurological event at which time it began to trend upward rapidly.

As described above, the computational demands of the processing provided by feature extractors 104 a, 104 b, 105 and classification provided by classifiers 106, 107 can be extensive. In the case of ambulatory systems supplied by portable power sources, such as implanted batteries, supplying the energy required to meet the computational demands can severely limit power source life. In some applications, physiological signals may be measured and analyzed continuously or often over long periods of time and the need to conserve energy may be particularly acute.

FIG. 3 depicts a simplified block diagram of a method of operating medical devices for analyzing signals that provides energy savings by scheduling measuring and analysis cycles based on the results of earlier measuring and analysis to reduce energy consumption and optimize system performance. One or more physiological signals from a subject are measured 110. In one embodiment, sixteen channels of physiological signals are measured. More or fewer channels may be measured according to the particular kinds of analysis being employed, the type of electrode arrays implanted, and the particularities of the subject's condition. The measured signals may be pre-processed, such as, for example, by amplification, filtering, and/or conversion from analog to digital, to generate input data 112 for analysis. Input data 112 may also comprise other subject dependent parameters (such as subject inputs and/or subject history data) that may be indicative and/or predictive of a subject's propensity for a neurological event.

Typically, the physiological signal(s) from the subject are measured during a sliding observation window or epoch. Characteristics of the sliding window may be adapted based on previous measurements and analysis. In particular, the sliding windows may operate continuously, periodically during specified intervals, or during an adaptively modified schedule (for example, to customize it to the specific subject's cycles). In some embodiments, adaptations to the sliding window can be made automatically by the system. In some embodiments, adaptations to the window may be made by a clinician. For example, if it is known that the subject is prone to have a neurological event in the morning, a clinician may program the system to continuously monitor the subject during the morning hours, while only periodically monitoring the subject during the remainder of the day. As another example, it may be less desirable to monitor a subject and provide an output to a subject when the subject is asleep. In such cases, the system may be programmed to discontinue monitoring or change the monitoring and communication protocol with the subject during a specified “sleep time” or whenever a subject inputs into the system that the subject is asleep or when the system determines that the subject is asleep. This could include intermittent monitoring, monitoring with a varying duty cycle, decreasing of the sampling frequency, or other energy saving or data minimization strategy during a time period in which the risk for a neurological event is low. Additionally, the system could enter into a low risk mode for a time period following each medication dose.

Input data 112 is subjected to an analysis 114. The analysis 114 may be performed by logic embodied in, for example, computer-executable instructions, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform particular tasks or implement particular abstract data types. Typically the functionality of the program modules may be combined or distributed as desired in various embodiments. The analysis 114 may comprise the application of one or more feature extractors 104 a, 104 b, 105 (FIG. 1) and one or more classifiers 106, 107 (FIG. 1) such as described above with respect to FIG. 1. Typically, the analysis 114 will comprise the application of a subset of available feature extractors applied to the input data to identify some characteristics of the signals. The output(s) from the first set of feature extractors may be combined using a first classifier.

Based on the analysis 114, an estimate of a susceptibility or propensity for the subject to have a neurological event is determined 116. The estimate may take the form of a qualitative characterization or may be represented quantitatively or by a combination of qualitative and quantitative characterizations. A qualitative characterization may, for example, relate to the presence of pre-onset characteristics of a neurological event. A quantitative characterization may, for example, be a single number, such as, for example, a probability of a neurological event occurring in a predetermined time period following the measurement of the signals or an estimated time horizon during which an estimated propensity for the subject to have a neurological event is below a predetermined threshold, or a collection of values that characterize the analysis.

Once the subject's propensity for a neurological event is estimated by the predictive system, a signal that is indicative of the estimated propensity for the future neurological event may optionally be communicated 118. In some embodiments, the predictive system provides an output that indicates when the subject has an elevated propensity for a neurological event. In such embodiments, the communication output to the subject may simply be a warning or a recommendation to the subject that was programmed into the system by the clinician. In other embodiments, the predictive system may output a graded propensity assessment, a quantitative assessment of the subject's condition, a time horizon until the predicted neurological event will occur, or some combination thereof. In such embodiments, the communication output to the subject may provide a recommendation or instruction that is a function of the risk assessment, probability, or time horizon.

Based on the results of the analysis and the estimate of the propensity of the subject to have a neurological event, succeeding measurement and analysis cycles are scheduled 120. In some embodiments, the measuring and analysis cycles are run less often when the estimate of the propensity indicates that it is relatively unlikely that a neurological event is imminent. On the other hand, if the estimate of the propensity indicates that a neurological event is relatively likely to be proximate, the measurement and analysis cycles are run more frequently, up to, possibly, continuously or some predetermined maximum rate or duty cycle. For example, instead of continuously monitoring the physiological signals with overlapping 5 second windows, if analysis of the patient's susceptibility determines a low susceptibility to a seizure, the analysis cycles may be reduced, to for example, analysis of a 5 second window each minute. However, if the subsequent analysis during the 5 second window during each minute indicates an increased susceptibility, the analysis cycles may go to an intermediate duty cycle (for example, 30 seconds every minute) or back to continuous monitoring. In some embodiments, the frequency of the measuring and analysis cycles may depend on the estimated time horizon during which an estimated propensity for the subject to have a neurological event is below a predetermined threshold, with the time between successive cycles preferably being between about one half and about one hundredth of the estimated time horizon. By varying the rate of measurement and analysis according to the estimated propensity, electrical energy savings are realized while maintaining sufficient monitoring vigilance.

Rather than or in addition to varying the frequency of measuring and analysis cycles, different analysis subsystems may be scheduled depending on the estimated propensity of the subject to have a neurological event. For example, in some embodiments, if the estimate of the propensity indicates that it is relatively unlikely that a neurological event is imminent, analysis subsystems having relatively lower computational demands will be scheduled. Preferably, such analysis subsystems will have a relatively high sensitivity, but not necessarily a high specificity. In general terms, the sensitivity of the analysis is related to the probability that the analysis indicates the presence of a condition given that the condition actually exists. In general terms, the specificity of the analysis is related to the likelihood or probability that the analysis indicates the absence of a condition given that the condition is actually absent. The detection of a line length of an EEG in the time window or energy of particular frequencies in measured signals are examples of feature extractors having a relatively low computational demand. Conversely, if the estimate of the susceptibility or propensity indicates that a neurological event is relatively likely to be proximate, subsystems having relatively greater computational demands could be scheduled.

Alternative embodiments may combine the two approaches described above. That is, both the frequency of measuring and analysis cycles and the choice of subsystems for analysis may be based on the estimate of the propensity. For example, if the estimated propensity is very low, then a subsystem having a relatively low computational demand, but preferably a high sensitivity, would be scheduled to run relatively infrequently. As the estimated propensity increases, either the computational intensity of the subsystems used or the rate at which the measurement and analysis cycle runs, or both, could be increased.

The criteria used for scheduling the next cycle 120 may be universal or may be adapted to a particular subject and/or to particular conditions of the subject. By way of examples, the criteria may include the presence or absence of certain characteristics in the signals or the exceedance of a threshold likelihood or probability. The criteria may be modified over time—either automatically by the system or manually by a clinician. The criteria may be adapted in response to various conditions of the subject such as, for example, the subject's state of wakefulness or current activity level, which may be determined through subject inputs or through analysis of the detected signals. The criteria may also adapted in response to current conditions of the medical device such as, for example, the current charge state of a battery. As an example, if the battery level is low, a warning may be provided to the subject and the measurement and analysis cycle may be scheduled to run less frequently.

In some alternative embodiments, a more computationally intensive analysis of signals already measured may be performed before undertaking a next cycle of measurement and analysis. FIG. 4 depicts a simplified block diagram of a method of operating medical devices in accordance with some of these alternative embodiments. The method depicted in FIG. 4 can be understood as a modification of the methods described above in relation to FIG. 3 and so details of steps common to the two methods that were described above will not be repeated here. One or more signals from a subject are measured 140. The signals may be pre-processed to generate input data 142 for analysis. Input data 142 may also comprise other subject dependent parameters (such as subject inputs and/or subject history data) that may be indicative or predictive of a subject's propensity for a neurological event.

Input data 142 is subjected to analysis 144 such as, for example, that analysis described above in connection with FIG. 3. Based on the analysis 144, an estimate of a susceptibility or propensity for the subject to have a neurological event is determined 146. A signal indicative of the estimated propensity may optionally be communicated 148.

The results of the first analysis 144 and the estimate of the propensity 146 are then examined 150 to determine whether they meet one or more specified criteria for further analysis. The specified criteria may be universal or may be adapted to a particular subject. By way of examples, the criteria may include the presence or absence of certain characteristics in the signals or the exceedance of a threshold. The criteria may be modified over time. The criteria may be adapted in response to various conditions of the subject such as, for example, the subject's state of wakefulness or current activity level, drug plasma levels, and the like. The criteria may also adapted in response to current conditions of the medical device such as, for example, the current charge state of a battery.

If the criteria for further analysis are not met, succeeding measurement and analysis cycles are scheduled 152 based on the results of the analysis and the estimated propensity. As described above in connection with FIG. 3, in some embodiments the rate at which measurement and analysis cycles are run may be varied, or different analysis subsystems may be scheduled, or both.

If the criteria in step 150 are met, for example if the estimate derived from the previous analysis indicates an heightened propensity for the monitored condition to exist or occur (for example, prediction of a pre-ictal condition), then another, higher level of analysis is performed 154 to determine a second estimate of a propensity for the subject to have a neurological event 156. The higher level analysis 154 may be performed by logic embodied in, for example, computer-executable instructions, such as program modules, executed by one or more computers or other devices.

Depending on the particular embodiment, the set of feature extractors employed in the higher level analysis 154 may be used in conjunction with or as an alternative to the set of feature extractors employed in the previous analysis 144. The set of feature extractors employed in the higher level analysis 154 will typically afford a higher level of computational complexity and/or may have a higher specificity and/or sensitivity than the set of feature extractors employed in the previous analysis 144 and/or may provide a different time resolution from the previous analysis 144. In one embodiment, the higher level analysis 150 may perform more refined versions of the analyses performed by the previous analysis 144. In another embodiment, the higher level analysis 154 may perform different kinds of analyses.

The output from the second set of feature extractors may be combined in the classifier used in the first stage analysis or a second classifier. The result from either classifier may, in one embodiment, have both an improved sensitivity and specificity, relative to the sensitivity and specificity of the classification based on only the first set of feature extractors. The second estimate 156 is preferably more refined than the first estimate 146 and may take the form of a qualitative characterization, a quantitative characterization, or a combination thereof.

A signal indicative of the new estimate of the propensity 156 may optionally be communicated 148. The results of the new analysis 154 and the new estimate of the propensity 156 are then examined to determine whether they meet one or more specified criteria for further analysis. The cycle of examination of results 150, higher level analysis 154, and further estimation of the propensity 156 can be repeated through as many levels of analysis as desired, until the results of the analysis 154 and the estimate of the propensity 156 no longer meet criteria for further analysis 150. At that point, succeeding measurement and analysis cycles are scheduled 152 as described above.

FIG. 5 depicts an illustrative example of a relationship between estimated propensity and the frequency of measuring and analysis cycles for one embodiment employing variable rate scheduling. Each bar represents a result of the predictive system as measuring and analysis cycles were run at various times, t₁, t₂, etc. At time t₁, a relatively low propensity of a neurological event was estimated and the next running of the analysis was scheduled for time t₂. A similarly low propensity was estimated at time t₂, and so the system was scheduled to be run again at time t₃, where the time interval t₃-t₂ is the same as the time interval t₂-t₁. At time t₄, an elevated propensity was estimated and so the analysis system was scheduled to execute more frequently, with a shorter interval between succeeding executions. The estimated propensity did not change again until the system was executed at time t₈, at which time the estimated propensity was once again lower and so the system was scheduled to run on the baseline schedule as it had been at t₁. At t₁₁, a highly elevated propensity was estimated, and so the system was scheduled to run on a much more frequent schedule which continued until a lowered propensity was estimated at time t₁₆.

The length and frequency of measurement and analysis cycles may be tailored to the prediction horizon. As an example, if the predictive system indicates that it is unlikely for a neurological event to occur in the next hour, the system could be scheduled to run more often than once per hour, but not so often as several hundred times per hour. Preferred scheduling frequencies would be between about 2 and about 100 times the reciprocal of the system's neurological event prediction horizon. By varying the frequency of measuring and analysis cycles according to the estimated propensity, energy savings would be realized.

In an alternative embodiment, different prediction subsystems may be run depending on the results of prior calculations. For example, if the current propensity for a neurological event is remote, a corresponding exceptionally low operating power analysis subsystem would be scheduled. If the low operating power analysis subsystem indicates an elevated propensity for a neurological event, a second, more computationally demanding and more specific subsystem would be scheduled. If the output of the second subsystem indicates a propensity above a certain threshold, a third subsystem may be scheduled, and so forth. In yet another alternative embodiment, the approaches of the two preceding embodiments may be combined. The selection of subsystems and their rates of execution may depend on the results of prior analyses.

The systems described herein may be embodied as software, hardware, firmware, or combinations thereof. In some instances, it may be desirable to have first or lower stage systems operating only in hardware in order to minimize energy requirements. The systems described above may be embodied in a device external to the subject, an implanted device, or distributed between an implanted device and an external device.

Because the methods of the present invention are able to consume less energy than conventional systems and will thereby prolong the life of the energy sources, the methods of the present invention will facilitate the long-term implementation of the systems in a portable and/or implantable device system. FIG. 6 illustrates a system in which the systems of the present invention may be embodied. The system 200 is used to monitor a subject 202 for purposes of measuring physiological signals and predicting neurological events. The system 200 of the embodiment provides for substantially continuous sampling of brain wave electrical signals such as in electroencephalograms or electrocorticograms, referred to collectively as EEGs.

The system 200 comprises one or more sensors 204 configured to measure signals from the subject 202. The sensors 204 may be located anywhere on the subject. In the exemplary embodiment, the sensors 204 are configured to sample electrical activity from the subject's brain, such as EEG signals. The sensors 204 may be attached to the surface of the subject's body (e.g., scalp electrodes), attached to the head (e.g., subcutaneous electrodes, bone screw electrodes, and the like), or, preferably, may be implanted intracranially in the subject 202. In one embodiment, one or more of the sensors 204 will be implanted adjacent a previously identified epileptic focus, a portion of the brain where such a focus is believed to be located, or adjacent a portion of a seizure network.

Any number of sensors 204 may be employed, but the sensors 204 will preferably include between 1 sensor and 16 sensors. The sensors may take a variety of forms. In one embodiment, the sensors comprise grid electrodes, strip electrodes and/or depth electrodes which may be permanently implanted through burr holes in the head. Exact positioning of the sensors will usually depend on the desired type of measurement. In addition to measuring brain activity, other sensors (not shown) may be employed to measure other physiological signals from the subject 202.

In an embodiment, the sensors 204 will be configured to substantially continuously sample the brain activity of the groups of neurons in the immediate vicinity of the sensors 204. The sensors 204 are electrically joined via cables 206 to an implanted communication unit 208. In one embodiment, the cables 206 and communication unit 208 will be implanted in the subject 202. For example, the communication unit 208 may be implanted in a subclavicular cavity of the subject 202. In alternative embodiments, the cables 206 and communication unit 208 may be attached to the subject 202 externally.

In one embodiment, the communication unit 208 is configured to facilitate the sampling of signals from the sensors 204. Sampling of brain activity is typically carried out at a rate above about 200 Hz, and preferably between about 200 Hz and about 1000 Hz, and most preferably at about 400 Hz. The sampling rates could be higher or lower, depending on the specific conditions being monitored, the subject 202, and other factors. Each sample of the subject's brain activity is typically encoded using between about 8 bits per sample and about 32 bits per sample, and preferably about 16 bits per sample.

In alternative embodiments, the communication unit 208 may be configured to measure the signals on a non-continuous basis. In such embodiments, signals may be measured periodically or aperiodically.

An external data device 210 is preferably carried external to the body of the subject 202. The external data device 210 receives and stores signals, including measured signals and possibly other physiological signals, from the communication unit 208. External data device 210 could also receive and store extracted features, classifier outputs, patient inputs, and the like. Communication between the external data device 210 and the communication unit 208 may be carried out through wireless communication. The wireless communication link between the external data device 210 and the communication unit 208 may provide a one-way or two-way communication link for transmitting data. In alternative embodiments, it may be desirable to have a direct communications link from the external data device 210 to the communication unit 208, such as, for example, via an interface device positioned below the subject's skin. The interface (not shown) may take the form of a magnetically attached transducer that would enable power to be continuously delivered to the communication unit 208 and would provide for relatively higher rates of data transmission. Error detection and correction methods may be used to help insure the integrity of transmitted data. If desired, the wireless data signals can be encrypted prior to transmission to the external data device 210.

FIG. 7 depicts a block diagram of one embodiment of a communication unit 208 that may be used with the systems and methods described herein. Energy for the system is supplied by a rechargeable power supply 224. The rechargeable power supply may be a battery, or the like. The rechargeable power supply 224 may also be in communication with a transmit/receive subsystem 226 so as to receive power from outside the body by inductive coupling, radiofrequency (RF) coupling, and the like. Power supply 224 will generally be used to provide power to the other components of the implantable device. Signals 212 from the sensors 204 are received by the communication unit 208. The signals may be initially conditioned by an amplifier 214, a filter 216, and an analog-to-digital converter 218. A memory module 220 may be provided for storage of some of the sampled signals prior to transmission via a transmit/receive subsystem 226 and antenna 228 to the external data device 210. For example, the memory module 220 may be used as a buffer to temporarily store the conditioned signals from the sensors 204 if there are problems with transmitting data to the external data device 210, such as may occur if the external data device 210 experiences power problems or is out of range of the communications system. The external data device 210 can be configured to communicate a warning signal to the subject in the case of data transmission problems to inform the subject and allow him or her to correct the problem.

The communication unit 208 may optionally comprise circuitry of a digital or analog or combined digital/analog nature and/or a microprocessor, referred to herein collectively as “microprocessor” 222, for processing the signals prior to transmission to the external data device 210. The microprocessor 222 may execute at least portions of the analysis as described herein. For example, in some configurations, the microprocessor 222 may run one or more feature extractors 104 a, 104 b, 105 (FIG. 1) that extract characteristics of the measured signal that are relevant to the purpose of monitoring. Thus, if the system is being used for diagnosing or monitoring epileptic subjects, the extracted characteristics (either alone or in combination with other characteristics) may be indicative or predictive of a neurological event. Once the characteristic(s) are extracted, the microprocessor 222 may transmit the extracted characteristic(s) to the external data device 210 and/or store the extracted characteristic(s) in memory 220. Because the transmission of the extracted characteristics is likely to include less data than the measured signal itself, such a configuration will likely reduce the bandwidth requirements for the communication link between the communication unit 208 and the external data device 210.

In some configurations, the microprocessor 222 in the communication unit 208 may run one or more classifiers 106, 107 (FIG. 1) as described above with respect to FIG. 1. The result 108 (FIG. 1) of the classification may be communicated to the external data device 210.

While the external data device 210 may include any combination of conventional components, FIG. 8 provides a schematic diagram of some of the components that may be included. Signals from the communication unit 208 are received at an antenna 230 and conveyed to a transmit/receive subsystem 232. The signals received may include, for example, a raw measured signal, a processed measured signal, extracted characteristics from the measured signal, a result from analysis software that ran on the implanted microprocessor 222, or any combination thereof.

The received data may thereafter be stored in memory 234, such as a hard drive, RAM, EEPROM, removable flash memory, or the like and/or processed by a microprocessor, application specific integrated circuit (ASIC) or other dedicated circuitry of a digital or analog or combined digital/analog nature, referred to herein collectively as a “microprocessor” 236. Microprocessor 236 may be configured to request that the communication unit 208 perform various checks (e.g., sensor impedance checks) or calibrations prior to signal recording and/or at specified times to ensure the proper functioning of the system.

Data may be transmitted from memory 234 to microprocessor 236 where the data may optionally undergo additional processing. For example, if the transmitted data is encrypted, it may be decrypted. The microprocessor 236 may also comprise one or more filters that filter out low-frequency or high-frequency artifacts (e.g., muscle movement artifacts, eye-blink artifacts, chewing, and the like) so as to prevent contamination of the measured signals.

External data device 210 will typically include a user interface 240 for displaying outputs to the subject and for receiving inputs from the subject. The user interface will typically comprise outputs such as auditory devices (e.g., speakers) visual devices (e.g., LCD display, LEDs, and the like), tactile devices (e.g., vibratory mechanisms), or the like, and inputs, such as a plurality of buttons, a touch screen, and/or a scroll wheel.

The user interface may be adapted to allow the subject to indicate and record certain events. For example, the subject may indicate that medication has been taken, the dosage, the type of medication, meal intake, sleep, drowsiness, occurrence of an aura, occurrence of a neurological event, or the like. Such inputs may be used in conjunction with the measured data to improve the analysis.

The LCD display may be used to output a variety of different communications to the subject including, status of the device (e.g., memory capacity remaining), battery state of one or more components of system, whether or not the external data device 210 is within communication range of the communication unit 208, a warning (e.g., a neurological event warning), a prediction (e.g., a neurological event prediction), a recommendation (e.g., “take medicine”), or the like. It may be desirable to provide an audio output or vibratory output to the subject in addition to or as an alternative to the visual display on the LCD.

External data device 210 may also include a power source 242 or other conventional power supply that is in communication with at least one other component of external data device 210. The power source 242 may be rechargeable. If the power source 242 is rechargeable, the power source may optionally have an interface for communication with a charger 244. While not shown in FIG. 8, external data device 210 will typically comprise a clock circuit (e.g., oscillator and frequency synthesizer) to provide the time base for synchronizing the external data device 210 and the communication unit 208.

Referring again to FIG. 6, in a preferred embodiment, most or all of the processing of the signals received by the communication unit 208 is done in an external data device 210 that is external to the subject's body. In such embodiments, the communication unit 208 would receive the signals from subject and may or may not pre-process the signals and transmit some or all of the measured signals transcutaneously to an external data device 210, where the prediction of the neurological event and possible therapy determination is made. Advantageously, such embodiments reduce the amount of computational processing power that needs to be implanted in the subject, thus potentially reducing energy consumption and increasing battery life. Furthermore, by having the processing external to the subject, the judgment or decision making components of the system may be more easily reprogrammed or custom tailored to the subject without having to reprogram the communication unit 208.

In alternative embodiments, the predictive systems disclosed herein and treatment systems responsive to the predictive systems may be embodied in a device that is implanted in the subject's body, external to the subject's body, or a combination thereof. For example, in one embodiment the predictive system may be stored in and processed by the communication unit 208 that is implanted in the subject's body. A treatment analysis system, in contrast, may be processed in a processor that is embodied in an external data device 210 external to the subject's body. In such embodiments, the subject's propensity for neurological event characterization (or whatever output is generated by the predictive system that is predictive of the onset of the neurological event) is transmitted to the external subject communication assembly, and the external processor performs any remaining processing to generate and display the output from the predictive system and communicate this to the subject. Such embodiments have the benefit of sharing processing power, while reducing the communications demands on the communication unit 208. Furthermore, because the treatment system is external to the subject, updating or reprogramming the treatment system may be carried out more easily.

In other embodiments, the signals 212 may be processed in a variety of ways in the communication unit 208 before transmitting data to the external data device 210 so as to reduce the total amount of data to be transmitted, thereby reducing the energy demands of the transmit/receive subsystem 226. Examples include: digitally compressing the signals before transmitting them; selecting only a subset of the measured signals for transmission; selecting a limited segment of time and transmitting signals only from that time segment; extracting salient characteristics of the signals, transmitting data representative of those characteristics rather than the signals themselves, and transmitting only the result of classification. Further processing and analysis of the transmitted data may take place in the external data device 210.

In yet other embodiments, it may be possible to perform some of the prediction in the communication unit 208 and some of the prediction in the external data device 210. For example, one or more characteristics from the one or more signals may be extracted with feature extractors in the communication unit 208. Some or all of the extracted characteristics may be transmitted to the external data device 210 where the characteristics may be classified to predict the onset of a neurological event. If desired, external data device 210 may be customizable to the individual subject. Consequently, the classifier may be adapted to allow for transmission or receipt of only the characteristics from the communication unit 208 that are predictive for that individual subject. Advantageously, by performing feature extraction in the communication unit 208 and classification in an external device at least two benefits may be realized. First, the amount of wireless data transmitted from the communication unit 208 to the external data device 210 is reduced (versus transmitting pre-processed data). Second, classification, which embodies the decision or judgment component, may be easily reprogrammed or custom tailored to the subject without having to reprogram the communication unit 208.

In yet another embodiment, feature extraction may be performed external to the body. Pre-processed signals (e.g., filtered, amplified, converted to digital) may be transcutaneously transmitted from communication unit 208 to the external data device 210 where one or more characteristics are extracted from the one or more signals with feature extractors. Some or all of the extracted characteristics may be transcutaneously transmitted back into the communication unit 208, where a second stage of processing may be performed on the characteristics, such as classifying of the characteristics (and other signals) to characterize the subject's propensity for the onset of a future neurological event. If desired, to improve bandwidth, the classifier may be adapted to allow for transmission or receipt of only the characteristics from the subject communication assembly that are predictive for that individual subject. Advantageously, because feature extractors may be computationally expensive and energy hungry, it may be desirable to have the feature extractors external to the body, where it is easier to provide more processing and larger power sources.

For additional energy savings, the systems of the present invention may also embody some of the energy saving concepts described in commonly owned, copending patent application Ser. No. 11/616,788, entitled “Low Power Device with Contingent Scheduling,” filed concurrently herewith, the complete disclosure of which is incorporated herein by reference.

More complete descriptions of systems that may embody the concepts of the present invention are described in commonly owned, copending U.S. patent application Ser. Nos. 11/321,897, 11/321,898, 11/322,150, all filed on Dec. 28, 2005, the complete disclosures of which are incorporated herein by reference.

The inventive aspects described herein may be applicable to commercial monitoring systems. For example, the systems herein may be applied to the NeuroPace® RNS system. Such commercial systems extract half-wave amplitude and duration, sum of absolute differences as an approximation of signal curve length (which is in turn a simplification of waveform fractal dimension), and a modified sum of absolute amplitudes as an approximation of signal energy. Instead of running all of the feature extractors continuously all of the time, subsequent measurement and analysis cycles may be scheduled based on analysis of previous measurement cycles. In some embodiments, the measuring and analysis cycles are run less often when the previous measurements indicates that it is relatively unlikely that a seizure is imminent. On the other hand, if previous measurements indicate that a seizure is relatively likely to be proximate, the measurement and analysis cycles are run more frequently, up to, possibly, some predetermined maximum rate. Such a feature extractor configuration will preserve computation power, reduce battery usage, and prolong the time between battery changes.

The ability to provide long-term low-power ambulatory measuring of physiological signals and prediction of neurological events can facilitate improved treatment regimens for certain neurological conditions. FIG. 9 depicts the typical course of treatment for a subject with epilepsy. Because the occurrence of neurological events 300 over time has been unpredictable, present medical therapy relies on continuous prophylactic administration of anti-epileptic drugs (“AEDs”). Constant doses 302 of one or more AEDs are administered to a subject at regular time intervals with the objective of maintaining relatively stable levels of the AEDs within the subject. Maximum doses of the AEDs are limited by the side effects of their chronic administration.

Reliable long-term essentially continuously operating neurological event prediction systems would facilitate epilepsy treatment. Therapeutic actions, such as, for example, brain stimulation, peripheral nerve stimulation (e.g., vagus nerve stimulation), cranial nerve stimulation (e.g., trigeminal nerve stimulation (“TNS”)), or targeted administration of AEDs, could be directed by output from a neurological event prediction system. One such course of treatment is depicted in FIG. 10. Relatively lower constant doses 304 of one or more AEDs may be administered to a subject at regular time intervals in addition to or as an alternative to the prophylactic administration of AEDs. Supplementary medication doses 306 are administered just prior to an imminent neurological event 308. By targeting the supplementary doses 306 at the appropriate times, neurological events may be more effectively controlled and potentially eliminated 308, while reducing side effects attendant with the chronic administration of higher levels of the AEDs.

While the present disclosure has been described in connection with various embodiments, illustrated in the various figures, it is understood that similar aspects may be used or modifications and additions may be made to the described aspects of the disclosed embodiments for performing the same function of the present disclosure without deviating therefrom. Other equivalent mechanisms to the described aspects are also contemplated by the teachings herein. Therefore, the present disclosure should not be limited to any single aspect, but rather construed in breadth and scope in accordance with the appended claims. 

What is claimed:
 1. A method of operating a medical device system, the method comprising: receiving heart rate signals from a subject using a sensor coupled to the subject so as to detect the heart rate signals from the subject; processing the heart rate signals received by the sensor using a programmed computer processing unit to detect at least one pre-onset characteristic of an epileptic event and determine a likelihood of the epileptic event based at least in part on the detected at least one pre-onset characteristic, wherein said processing the heart rate signals comprises performing a high-power processing of a first set of the heart rate signals received by the sensor; and when the detected at least one pre-onset characteristic subsequently indicates a low likelihood of the epileptic event, performing a low-power processing of a second set of the heart rate signals received by the sensor subsequent to the first set of the heart rate signals.
 2. The method as recited in claim 1 wherein said performing the low-power processing comprises determining a time to perform the low-power processing of the second set of the heart rate signals.
 3. The method as recited in claim 2 wherein said determining the time to perform the low-power processing is based at least in part on a charge condition of a battery associated with the medical device system.
 4. The method as recited in claim 2 wherein said determining the time to perform the low-power processing is based at least in part on a measurement indicative of sleep.
 5. The method as recited in claim 2 wherein said determining the time to perform the low-power processing is based at least in part on the time of day.
 6. The method as recited in claim 2 wherein said determining the time to perform the low-power processing further comprises selecting at least one analysis system for performing the low-power processing of additional heart rate signals based at least in part on the detection of pre-onset characteristics of the epileptic event.
 7. The method as recited in claim 1 wherein processing the heart rate signals comprises performing an analysis on the heart rate signals to generate an estimated propensity for the subject to have the epileptic event within a specified time horizon.
 8. The method as recited in claim 7 wherein said performing the analysis to generate the estimated propensity comprises generating an estimated time horizon during which an estimated likelihood that the subject will have the epileptic event is below a predetermined threshold.
 9. The method as recited in claim 8 wherein said performing the low-power processing of the second set of the heart rate signals comprises determining a scheduled time to perform the low-power processing, the determination of the scheduled time being based on the estimated time horizon.
 10. The method as recited in claim 9 wherein the scheduled time is between one half and one hundredth of the estimated time horizon.
 11. The method as recited in claim 7 wherein the performing the analysis on the heart rate signals to generate the estimated propensity comprises: performing a first analysis to estimate a first propensity for the subject to have the epileptic event; and performing a second analysis to estimate a second propensity for the subject to have the epileptic event when the first estimate meets one or more specified criteria.
 12. The method as recited in claim 1 wherein: said low-power processing of the second set of the heart rate signals comprises processing the second set of the heart rate signals using a first analysis having a first computational demand level; and said high-power processing of signals comprises processing the first set of the heart rate signals using a second analysis having a second computational demand level, wherein the second computational demand level is greater than the first computational demand level.
 13. The method as recited in claim 1 wherein: said low-power processing the second set of the heart rate signals comprises processing the second set of the heart rate signals according to a first processing frequency; and said high-power processing of the second set of the heart rate signals comprises processing the second set of the heart rate signals according to a second processing frequency, wherein the second processing frequency is greater than the first processing frequency.
 14. The method as recited in claim 1 wherein the likelihood of the epileptic event is a continuum extending from a minimum probability to a maximum probability, the low likelihood of the epileptic event corresponding to the minimum probability.
 15. The method as recited in claim 1 further comprising: performing a high-power processing of a third set of the heart rate signals received by the sensor subsequent to the second set of the heart rate signals when the detected at least one pre-onset characteristic subsequently indicates a high likelihood of the epileptic event.
 16. The method as recited in claim 15 wherein the likelihood of the epileptic event is a continuum extending from a minimum probability to a maximum probability, the low likelihood of the epileptic event corresponding to the minimum probability, the high likelihood of the epileptic event corresponding to the maximum probability.
 17. A method comprising: receiving electrical signals from a subject using a heart rate sensor coupled to the subject so as to detect heart rate signals from the subject; processing the electrical signals received by the heart rate sensor using a programmed computer processing unit to detect at least one pre-onset characteristic of an epileptic seizure and determine a likelihood of seizure based at least in part on the detected at least one pre-onset characteristic, wherein said processing the electrical signals comprises performing a high-power processing of a first set of the electrical signals received by the heart rate sensor; and when the detected at least one pre-onset characteristic indicates a low likelihood of seizure, performing a low-power processing of a second set of the electrical signals received by the heart rate sensor subsequent to the first set of the electrical signals.
 18. The method as recited in claim 17 wherein said processing the electrical signals comprises performing an analysis on the electrical signals to estimate a propensity for the subject to have an epileptic seizure within a specified time horizon.
 19. The method as recited in claim 18 further comprising communicating information related to the estimate of a propensity to the subject.
 20. The method as recited in claim 18 wherein performing the analysis to estimate the propensity comprises estimating a time horizon during which an estimated likelihood that the subject will have an epileptic seizure is below a predetermined threshold.
 21. The method as recited in claim 17 wherein processing the electrical signals comprises applying at least one feature extractor to the electrical signals.
 22. The method as recited in claim 17 wherein: said low-power processing of the second set of the electrical signals comprises processing the second set of the electrical signals using a first analysis having a first computational demand level; and said high-power processing of the first set of the electrical signals comprises processing the first set of the electrical signals using a second analysis having a second computational demand level, wherein the second computational demand level is greater than the first computational demand level.
 23. The method as recited in claim 17 wherein: said low-power processing of the second set of the electrical signals comprises processing the second set of the electrical signals according to a first processing frequency; and said high-power processing of the first set of electrical signals comprises processing the first set of the electrical signals according to a second processing frequency, wherein the second processing frequency is greater than the first processing frequency. 